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The integration of augmented reality (AR) metaverses with federated learning presents a revolutionary approach to glioblastoma diagnostics. This study explores the design and implementation of an AR-driven metaverse ecosystem that connects decentralized datasets across institutions while maintaining patient privacy and data security. By leveraging federated learning, the system enables collaborative development of advanced diagnostic algorithms without direct data sharing. The AR interface enhances real-time visualization of multimodal diagnostic outputs, including imaging, spectral, and histopathological data, facilitating improved clinician understanding and decision-making. The metaverse environment promotes global collaboration among researchers, clinicians, and AI models, fostering an innovative community for glioblastoma research and diagnosis. Preliminary results highlight improved diagnostic accuracy and accessibility, with potential to reduce disparities in glioblastoma care. This work lays the foundation for a scalable, interactive, and patient-centric platform that redefines diagnostics in neuro-oncology, empowering stakeholders with immersive technologies and robust analytics.jdavids@ic.ac.uk